A commonality in many of these approaches is that they model the problem of entity coreference as a collection of decisions between mention pairs (Bagga and Baldwin, 1999;Soon et al., 2001;McCallum and Wellner, 2004;Singla and Domingos, 2005;Bengston and Roth, 2008).

due to the paucity of labeled training data, we did not estimate parameters from data, but rather set the compatibility functions manually by specifying their log scores.


The incremental improvements also show that each Web feature type brings in some additional benefit over the information already present in the Reconcile baseline, which includes alias, animacy, named entity, and WordNet  Table 3: Primary test results on the ACE04, ACE05, and ACE05-ALL datasets.

as a real example from our development set, the cooccurrence count c 12 for the headword pair (leader, president) is 11383, while it is only 95 for the headword pair (voter, president); after normalization and log 10 , the values are -10.9 and -12.0, respectively.


Hatzivassiloglou and McKeown (1997) proposed a method to identify the polarity of adjectives based on conjunctions linking them.

such statistics may not be reliable when the corpus size is small.


There are also lots of studies for cross-domain sentiment analysis (Blitzer et al., 2007;Tan et al., 2007;Li et al., 2009;Bollegala et al., 2011;He et al., 2011;Glorot et al., 2011).

as an example shown in Figure 2, we can extract two paths or relationships between topic and sentiment words from the dependency tree of the sentence "The movie has good script": "NNamod-JJ" from "script" and "good", and "NN-nsubj-VB-dobj-NN-amod-JJ" from "movie" and "good".


turn left, turn right, walk forward) and not high-level concepts (e.g.

one thing to note though is that while SGoLL makes the lexicon learning step much faster and scalable, another bottleneck in the overall system is training the semantic parser.


For all comparisons to the Chen and Mooney results, we use the performance of their refined landmarks plans system which performed the best overall.

the remaining refined landmarks plans are then treated as supervised training data for a semantic-parser learner, KRISP (Kate and Mooney, 2006).


The bottleneck of their algorithm is the intersection process which is time-consuming to perform.

we concentrate our attention on only candidate meanings that are less than a certain size.


(2007) 65.75 Heilman and Smith (2010) 62.8 Our baseline (PR) 62.0 Our method (ps-SRK) 64.6 Our method (pw-SRK) 63.8 Our method (kb-SRK) 65.1  neering efforts.

the performance of kb-SRK reaches the peak when window size becomes two.


During the last couple of years, topic models such as Probabilistic Latent Semantic Analysis (Hofmann, 1999) and Latent Dirichlet Allocation model (Blei, 2003), have drawn more and more attention and been applied successfully in NLP community.

it is infeasible in our model because some phrases occur in C f out while being absent in C f in .


Interestingly, on these pairs, PSMT generally prevails over tree-based SMT 1 , producing overall highquality outputs and isolated but critical reordering errors that undermine the global sentence meaning.

they are unsuitable to model long reordering as they classify as "discontinuous" every phrase that does not immediately follow or precede the last translated one.


On average, there are fewer parses per sentence, showing that hashing is eliminating many equivalent parses for more ambiguous sentences.

as we combine partial trees to build the derivation, we need to convolve the hash values in a consistent manner.


Since a lexical item is a letter of the string alphabet, each production of a lexicalized grammar produces at least one letter of the generated string.

if we change the lexicalization construction as indicated before this example, then all the productions S δ (x 1 ) → A δ (δ , δ , x 1 ) are replaced by the productions S δ (x 1 ) → A(x 1 , δ).


In fact, they prove that TAG cannot even strongly lexicalize the weaker tree insertion grammars (Schabes and Waters, 1995).

formally, the set T Σ (X) of Σ-trees indexed by X is the smallest set T such that X ⊆ T and σ(t 1 , .


The nonterminals of regular tree grammars only occur at the leaves and are replaced using first-order substitution.

the monadic variant is strongly equivalent to a slightly extended version of TAG, which is called non-strict TAG (Kepser and Rogers, 2011).


(2011) who use weighted linear combination (WLC) to grade the relevance of a tweet given a query.

to PageRank, DivRank assumes that the transition probabilities change over time.


We find that this is because with TimeUserLDA, we can remove tweets that are considered personal and therefore do not contribute to bursty topic ranking.


They provide statistically significant improvements in review and segment F 1 , as well as accuracy, over the baseline models.

reviews posted by other users provide a great resource for mining such information.


Rather than ratable aspects that are common across the entire domain (e.g., "ingredient", "cooking method"), we are interested in actionable information that is related and specific to the subject of the review.

in this paper, we propose a generative model that jointly identifies user-proposed refinements in instruction reviews at multiple granularities, and aligns them to the appropriate steps in the original instructions.


Indeed, in the experiments we present in Section 5.3, mapping is not necessary for RS-MixHMM.

rSA-MixHMM generates segments using a mixture of the multinomial distribution for the segment label z j and the (fixed) multinomial distribution 3 for the step s a j .


The refinement emission distributions for S-HMM and RS-MixHMM are fairly similar, but the probabilities of several key terms like added, used, and instead are higher with RS-MixHMM.

of this setup, their model reduces to our S-Mix baseline in a fully unsupervised setting.


For this example, in order to get the complete question, we would need to know that sentence S3 is a question that inquires for more details about the problem asked earlier, instead of stating its own question.

unlike other structured knowledge bases, often it is not straightforward to extract information such as questions and answers in online forums because such information spreads in the conversations among multiple users in a thread.


Thus, we define the replied question Qr i as the sub question with the maximal similarity to sentence x i : It is intuitive that different summary sentences aim at answering different sub questions.

it performs very well in simple factoid QA settings, where the answers to factoid questions often relate to a single named entity like a person, time or location.


Our main contributions are that we proposed a systematic way for modeling semantic contextual interactions between the answer sentences based on question segmentation and we explored both the textual and nontextual answer features learned via a group L 1 regularization.

there is no previous work that explores the complex multi-sentence question segmentation and its contextual modeling for community answer summarization.


Tsuruoka and Tsujii (2005) proposed easiest-first deterministic decoding.

the slightly worse sequences can be found by the exact heuristic derived from the first Viterbi forward pass.


1: lb = best score from beam search 2: init lattice 3: for i=0;;i++ do 4: Compute α(y i ) and β(y i ) according to Equations (6) and 73: prune y i from the current lattice 5: Figure 2: Column-wise lattice expansion: (a) The best sequence of the initial degenerate lattice, which does not pass through the degenerate label in the first column.


This property ensures the efficiency of the iterative Viterbi algorithm.

to have a tighter (higher) lower bound lb is important.


When performing unsupervised learning for the target language, we need to marginalize over possible derivations.

likewise, the threshold value b for the PR constraint on the dependency length is tuned on the source languages, using average test set accuracy as the selection criterion.


"Laboratory examples" of mentioned language (such as the examples thus far in this paper) only begin to illustrate the variation in the phenomenon.

a property might be a token's spelling, pronunciation, meaning (for a variety of interpretations of meaning), structure, connotation, original source (in cases of quotation), or another aspect for which language is shown or demonstrated.


From 3,831 articles, a set of 898 sentences were found to contain 1,164 candidate instances that passed the combination of stylistic and lexical filters.

limitations in the Anderson corpus' structure (particularly lack of word-or phrase-level annotations) and content (the authors admit it is noisy) served as compelling reasons to start afresh and create a richer resource.


Calculations first were performed to determine the level of agreement on the mere presence of mentioned language, by mapping labels WW, NN, SP, and OM to true and XX to false.

the per-category results showed reduced levels of agreement.


Clearly, useless nonterminals can be eliminated from any head grammar without affecting the language of the grammar.

ba 19 c 17 must be a substring of v i,1 = w i+1,1 , which shows that ( †) holds with i + 1 in place of i.


This is because the model did not see enough evidence to put them in the same row.

5 Given the response and reference tables, x res and x ref , we can compute: where i and j denote rows, Match(i, j) is one if i and j are matched to each other in the optimal matching or zero otherwise.


It shows that in some cases context information is not adequate, and a possible improvement might be obtained by providing more context to the model.

we first match words in mentions of type person against a regular expression to recognize entity attributes from a fixed set of titles and suffixes, and the first, middle and last names.


"He is from Bulgaria" versus "He is Bulgarian").

the last section in the table contains sample features with their values.


Although our system is actually able to produce a limitless number of results for a given input, we limited the number of outputs for each input to reduce the effort required for the annotation task.

after the ingredient list is complete, the phonetic module analyzes all ingredient pairs to generate neologisms with possibly homophonic puns based on phonetic similarity.


Our binary observations are written as 1, .

the pattern, "[Person] family moved from [Location]", which does not express place of birth, had low φ rs in spite of having higher n rs /N s than the valid pattern "[Person] native of [Location]".


We evaluated the performance by precision, recall, and F value.

a knowledge base is a set of relation instances about predefined relations.


In (Bellare and McCallum, 2007;Hoffmann et al., 2011), they used multiinstance learning, which deals with uncertainty of labels, to relax the DS assumption.

given the labeled corpus created with the DS assumption, we first predict whether each pattern, which frequently appears in text to express a relation (see Section 4), expresses a target relation.


Chronologies hand-written by journalists are a very useful resources for evaluation of our system, as they are completely dissociated from our research and are an exact representation of the output we aim to obtain.

our 91 chronologies were randomly divided into 4 sub-samples, each of them being used once as test data.


Table 2 shows the result of our method comparing with the traditional system and the topic-lexicon specific translation method described as above.

they first assign a specific topic for the document to be translated.


A link (i, j) means a source-side position i aligns to a target-side position j.

the monolingual data for training English language model includes the Xinhua portion of the GIGAWORD corpus, which contains 238M English words.


The k-th dimension P (z = k|d) means the probability of topic k given document d. Different from rule-topic distribution, the documenttopic distribution can be directly inferred by an offthe-shelf LDA tool.

in addition to the traditional hiero system, we also compare with the topic-specific lexicon translation method in Zhao and Xing (2007).


After extraction, we get a set of instances I = {(r, P (z|d), c)} with different document-topic distributions for each rule.

rule (c) contains an even distribution over various topics.


The process of rule-topic distribution estimation is analogous to the traditional estimation of rule translation probability (Chiang, 2007).

we denotes each sentence pair by (z f , z e , a), where z f and z e are the topic assignments of source-side and target-side sentences respectively, and a is a set of links {(i, j)}.


Conjunction scope may benefit from parallelism and higher-level structure, which is easily accessible when joining two matching non-terminals in a context-free grammar, but much harder to determine in the local views of graph-based dependency parsers.

with edge-based scoring, the object is not accessible when scoring where the preposition should attach, and PP-attachment is known to depend on the object of the preposition (Hindle and Rooth, 1993).


(Xue and Yang, 2011) presented results on a binary classification of whether or not a comma marks a sentence boundary, while the present work addresses a multi-category classification problem aimed at identifying discourse segments and preliminary discourse relations anchored by the comma.

to evaluate the performance of our model on this problem, we recomputed the results by putting IP COORD and SB in one category, putting VP COORD in another category and the rest of the labels in a third category.


In the first method, comma disambiguation is integrated into the parsing process while in the second method we train a supervised classifier to classify the Chinese comma, using features extracted from automatic parses.

syntactically annotated treebanks often contain important clues that can be used to infer discourse-level information.


Vieira and Poesio (2000) describe heuristics for processing definite descriptions in news text.

fine-grained versions distinguish between the categories old, the six mediated subtypes, and new.


We first describe the dataset used for these experiments and then provide results for property extraction and property name synonym discovery.

it is more likely that two property names that are not synonyms appear together within a single description.


This filtering process produces a projected training corpus, denoted as D x , which contains only terms that are present in x.

we employed two different Twitter collections.


Then, the values for α(y i , ) are sorted in ascending order.

the other collection, SOCCER tEAMS, contains 6 large-scale PU corpora (St 1 to St 6 ), taken from a platform for real time event monitoring (the link to this platform is omitted due to blind review).


B-SVM employs a single transition threshold α min for the entire corpus, instead of a different threshold α x min for each x ∈ D. It is representative of the state-of-the-art for learning classifiers from PU data.

we propose as an alternative the use of Lazy Associative Classifiers (Veloso et al., 2006).


always corresponds to the PAGES field, while proceedings is always associated with BOOKTITLE or JOURNAL), or as certain global regulations about complete structures (e.g., at least one word should be tagged as verb when performing a sentence-level POS tagging).

in the figure, for example, the segments C 1 and C 3 are identified as two argument segments (which are mentions of types T 1 and T 3 respectively) and are mapped to two "nodes", and the segment C 2 is identified as a gap segment that connects the two arguments A 1 and A 3 .


However, encoding such information as a general constraint can be inappropriate, as potentially better structures can be found if one considers other alternatives.

structured prediction with limited supervision is a popular topic in natural language processing.


We choose to use L-BFGS for all our experiments in this paper.

the label of each gap segment is uniquely determined by its surrounding argument segments with a list representation.


Figure 2 presents this information for an example utterance.

exploiting social cues improves the performance of all models.


Perhaps caregivers are following in, i.e., talking about objects that their children are interested in (Baldwin, 1991).

each utterance in the corpus contains the follow-ing information: • the sequence of orthographic words uttered by the care-giver, • a set of available topics (i.e., objects in the nonlinguistic objects), • the values of the social cues, and • a set of intended topics, which the care-giver refers to.


Psychological research on subjective evaluation [35], as well as initial experiments using ourselves as subjects, indicated that this pairwise set-up easier to work with than simply presenting a single sentence and asking whether it is memorable or not; the latter requires agreement on an "absolute" criterion for memorability that is very hard to impose consistently, whereas the former simply requires a judgment that one quote is more memorable than another.

memorable quotes differ in other interesting aspects as well, such as sound distributions.


In their patterns of verb tenses, personal pronouns, and determiners, memorable quotes are structured so as to be more "free-standing," containing fewer markers that indicate references to nearby text.

there is a rough consensus that an appropriate definition involves elements of both recognition -people should be able to retain the quote and recognize it when they hear it invoked -and production -people should be motivated to refer to it in relevant situations [15].


Our monolingual Corpus is also crawled from the web.

ccLN(trying) is 5 because (e 4 j 2 , e 5 j 1 ) falls under its child node play, thus does not count towards ccLN of trying.


In the WMT shared tasks, many new generation metrics, such as METEOR (Banerjee and Lavie, 2005), TER (Snover et al., 2006), and TESLA  have consistently outperformed BLEU as judged by the correlations with human judgments.

we note in Figure 2 that the trigram 买雨伞 and the bigram 买伞 are still unmatched, even though the match between 雨伞 and 伞 should imply the match between 买雨伞 and 买伞.


Segmenting Chinese sentences into words is a natural language processing task in its own right (Zhao and Liu, 2010;Low et al., 2005).

in addition, machine translation system parameters are tuned by maximizing the automatic scores.


We plan to conduct experiments on German and other Asian languages with the same linguistic phenomenon in future work.

tESLA-M found discounting function words very effective for English and other European languages such as German.


Birch and Osborne (2011) use either Hamming Distance or Kendall's τ Distance (Kendall, 1938) in their metric LRscore, thus obtaining two versions of LRscore.

pORT incorporates precision, recall, strict brevity penalty and strict redundancy penalty, plus a new word ordering measure v. As an evaluation metric, pORT performed better than BLEU at the system level and the segment level, and it was competitive with or slightly superior to METEOR at the segment level.


We constrain the forbidden types of relation to become one-to-one, as in (Birch and Osborne, 2011).

the ordering measure v s is the harmonic mean of v 1 and v 2 : (15) v s in (15) is computed at segment level.


We believe this is because of the strict redundancy penalty in Qmean.

we have found that ρ punishes long-distance reorderings too heavily.


We learn separate weights for relative-frequency and lexical estimates for both p m (ē|f ) and p m (f |ē).

this enables us to generate new sentences that cannot be generated by component models.


In the typical log-linear model SMT, the posterior probability for each phrase pair (ē,f ) is given by: Ensemble decoding uses the same framework for each individual system.

it requires all component models to fully decode each sentence, compute n-gram expectations from each component model and calculate posterior probabilities over translation derivations.


We consider it trivial, however, to specify such a narrow problem within our formulation, and it will lead to higher performance than that of the reported results, in any case.

since formula (1) of §2 is based on adding the description length, it is important that the whole value be additive to enable efficient optimization (as will be explained in §4).


Given a multilingual text, we evaluate the outputs B and L through the following scores: P B /R B : Precision/recall of the borders detected (i.e., the correct borders detected, divided by the detected/correct border).

we trained the model only through monolingual data, as mentioned above.


Monolingual texts were also used to build multilingual texts, as explained in the following subsection.

our work considers more than 200 languages, and the portions of embedded text are larger: up to the paragraph level to accommodate the reality of multilingual texts.


it is risky to directly replace the input sentence with a paraphrased sentence, since the errors in automatic paraphrase substitution may jeopardize the translation result seriously.


Otherwise the difference in CTR is determined by the "premium position" rather than by the independent variable under investigation (presence/absence of particular affective terms in the text ad).

the advantages are fast experiment building and evaluation, fully-automated analysis, and low cost.


The two variations were the same as in the first experiment for the control condition "gentle knight", and for the experimental condition "valorous knight" (see Figure 2 for the complete ads).

we were interested in gaining insights about a system for affective variations of existing commentaries on medieval frescoes for a mobile museum guide that attracts the attention of visitors towards specific paintings (Guerini et al., 2008;Guerini et al., 2011b).


We now elaborate the construction of C (w, p).

we define consistency among the polarities of words/synsets in a dictionary and give methods to check it.


The verb intoxicate has three synsets in WordNet, each with the same frequency.


Simple Wikipedia he was sentenced to twenty-five years to life in prison in 1981 .

(2010) in the sense that we do not take syntactic information into account; we rely on PBMT to do its work and implicitly learn simplifying paraphrasings of phrases.


We have shown that cost sensitive learning can be applied to POS tagging only with multiclass SVMs.

this definition follows the fact that POS tags in the same category form similar syntax structures in a sentence (Zhao and Marcus, 2009).


They achieve an accuracy of 96.08% and around 93.9 F1-score.

especially inter-category POS errors seriously affect the performance of chunking because they are more likely to mislead the chunk compared to intra-category errors.


Because segmentation using a dictionary alone can serve as a strong baseline in Chinese word segmentation (Sproat et al., 1996), the use of dictionaries is expected to make our joint model more robust and enables us to investigate the contribution of the syntactic dependency in a more realistic setting.

we have also found that we must balance the learning rate between features for segmentation and tagging decisions, and those for dependency parsing.


In beam search, we use the step index that is associated with each state: the parser states in process are aligned according to the index, and the beam search pruning is applied to those states with the same index.

in processing natural languages that do not include delimiters (e.g.


In the current systems, the existence of these characters causes numerous oversegmentation errors for OOV words.

for the beam search to function effectively, all states with the same index must be comparable, and all terminal states should have the same step index.


To investigate this point, we define the feature vector φ and score Φ of the  (W20, W21: T03,04 q−1.w c0 * q−1 and q−2 respectively denote the last-shifted word and the word shifted before q−1.

because they regarded word segmentation as given, their model did not consider the interaction between segmentation and POS tagging.


More importantly, it extends the existing works into a more general framework of constituent parsing to utilize more lexical and structural context and incorporate more strength of various parsing techniques.

for parser combination, we follow the setting of fossum and Knight (2009), using Section 24 instead of Section 22 of WSJ treebank as development set.


They are: Berkeley (Petrov et al., 2006), Bikel (2004), Charniak (2000) and Stanford  chineseFactored, which is also the default used by Stanford dependencies.

our comparison of performance and efficiency across seven popular open source parsers (four constituent and three dependency) shows, by contrast, that recent higher-order graph-based techniques can be more accurate, though somewhat slower, than constituent parsers.


Every parser was run with its own default options.

we compared seven popular open source parsersfour constituent and three dependency -for generating Stanford dependencies in Chinese.


Phrase-only LM look-ahead pruning defines the look-ahead score q LMLA (ẽ|ẽ ) = q LME (ẽ) to be the LM score of phraseẽ, assumingẽ to be the full sentence.

we compare our decoder to Moses, reaching a similar highest BLEU score, but clearly outperforming it in terms of scalability with respect to the trade-off ratio between translation quality and speed.


Without the proposed extensions, Moses slightly outperforms our decoder in terms of BLEU.

we also run comparisons with the Moses decoder (Koehn et al., 2007), which yields the same performance in BLEU, but is outperformed significantly in terms of scalability for faster translation.


The LM probabilities are stored in a trie, where each node corresponds to a specific LM history.

the latter already scales better for higher speed.


In order to separate out the individual contributions of the novel HD-HRs and NRRs, we carry out an additional experiment (HD-HR+Glue) using HD-HRs with monotonic glue rules only (adjusted to refined rule labels, but effectively switching off the extra reordering power of full NRRs).

examining translation rules extracted from the training data shows that there are 72,366 types of non-terminals with respect to 33 types of POS tags.


hD-hRs are only applied to generate derivations spanning no more than K words -the initial phrase length limit used in training to extract hD-hRswhile NRRs are applied to derivations spanning any length.


Different application scenarios of paraphrase have different demands on the paraphrasing results and up to now, the widely mentioned criteria include (Zhao et al., 2009;Chen and Dolan, 2011;Metzler et al., 2011): Semantic adequacy, fluency and dissimilarity.

the results show that our method is able to effectively control the self-paraphrase rate and lower down the score of self-BLEU, this is done by both of the process of joint learning and introducing the metric of iBLEU to avoid trivial self-paraphrase.


In this paper, our focus is to study how to represent temporal documents effectively for event detection.

we used the event detection method in (Swan and Allan, 2000) as baseline, denoted as timeminesχ 2 .


The most related work to ours is the boostVSM introduced by (He et al., 2007b), it proposes to weight different term dimensions with corresponding bursty scores.

built on bursty features, our representation model can well adapt to text streams with complex trends, and therefore provides a more reasonable temporal document representation.


But these techniques still face cost problems when preparing quality seed examples, which plays a crucial role in obtaining good extractions.

these automatic annotations can be unreliable because of source text mis-classification and word alignment errors; thus, it can cause a critical falling-off in the annotation projection quality.


There is significantly more negative language than positive language in CD when compared with the ratios of negative to positive language in WIKI, which are almost the same.


We use OpinionFinder (Wilson et al., 2005) which employs negative and positive polarity cues.

as a simplifying assumption, similar to the work of (Hassan et al., 2010), we adopt the view that replies in the sentences that are determined to be attitudinal and contain secondperson pronouns (you, your, yourself) are assumed to be directed towards the recipients of the replies.


Our corpus for ranking consisted of 47 clinical narratives obtained from the medical center and annotated with MEs, temporal expressions, relations and event chains.

the distribution of chains across different types of clinical narratives is shown in Figure 2.


Rather than use a wildcard * in both positions (ADJ and NOUN, or VERB and NOUN), which gives limited results with a search engine like Google, we generate fully instantiated similes from hypotheses generated via the Google n-grams (Brants & Franz, 2006).

these labels are then propagated to related concepts (e.g., excitement is propagated from rollercoasters to amusement parks) so that the implicit affect of many other concepts can be determined.


For the first layer, a large collection of stereotypical descriptions is harvested from the web.

for example: disease, poison, sin, crime, ideology, weapon, …} src(Hitler) = {monster, criminal, tyrant, idiot, madman, vegetarian, racist, …} Let srcTypical(T) denote the aggregation of all properties ascribable to T via metaphors in src(T): We can also use the posTypical and negTypical variants in (7) and (8) to focus only on metaphors that project positive or negative qualities onto T. In effect, (9) provides a feature representation for a topic T as viewed through the prism of metaphor.


We therefore propose a blocked sampling algorithm that samples words rather than letters in the plaintext, as follows: 1.

on the long ciphertexts, our algorithm gets close to perfect decipherment, surpassing the Viterbi algorithm by a large margin.


Finally, we confirm the wellknown result that MNB is normally better and more stable than multivariate Bernoulli NB, and the increasingly known result that binarized MNB is better than standard MNB.

if not, we split at spaces for unigrams, and we filter out anything that is not [A-Za-z] for bigrams.


All results reported use α = 1, C = 1, β = 0.25 for NBSVM, and C = 0.1 for SVM.

nBSVM performs well on snippets and longer documents, for sentiment, topic and subjectivity classification, and is often better than previously published results.


NBSVM performs well on snippets and longer documents, for sentiment, topic and subjectivity classification, and is often better than previously published results.

the standard 2000 full-length movie review dataset (Pang and Lee, 2004).


Using β ∈ [¼, ½] makes the NBSVM more robust than more extreme values.

as in (Metsis et al., 2006), we find that binarizing f (k) is better.


CHILDES is a collection of corpora from many studies of child language based on episodic speech data.

the definition of first-language developmental metrics has as yet been left up to human reasoning.


Note that the Berkeley parser is trained on the Penn treebank (Marcus et al., 1994)    2008).

the number of flat/hierarchical rules generated by using Malt trees is the lowest.


These disadvantages are a consequence of failing to exploit the generalisations that CCG combinators define.

one interesting comparison is between the PTB parser of Petrov and Klein (2007) Clark and Curran (2007) 90.9 85.5 88.8 Fowler and Penn (2010) 90.9 86.0 89.3 Auli and Lopez (2011) 91.7 86.2 89.5 Native PTB Klein and Manning (2003) 89.8 85.8 - Petrov and Klein (2007) 93.6 90.1 - Charniak and Johnson (2005) 94.8 91.5 - Table 5: F-scores on section 23 for PTB parsers and CCG parsers with their output converted by our method.


Depending on the diversity of training corpus, a varying number of underlying topics may be appropriate.

given a parallel training corpus T composed of documents d i , we build a source side topic model over T , which provides a topic distribution p(z n |d i ) for z n = {1, .


Results are compared with two pivoting approaches, checking for entailment between the original English texts and the translated German hypotheses.

the performance increase brought by DR features over PT is minimal.


Evaluation based on exact match against a single reference answer leads to under-estimation, because an English phrase is often translated into several Chinese counterparts.

the φ 2 score proposed by Gale and Church (1991) is used to measure the correlations between English and Chinese tokens: where e is an English word and f is a Chinese character.


Since observed words in definitions are too few to reveal the semantics of senses, missing words can be used to tell the model what the definition is not about.

finally we also show how to use WN neighbor sense definitions to construct a nuanced sense similarity wmfvec, based on the inferred latent semantic vectors of senses.


For example, the jcn similarity measure (Jiang and Conrath, 1997) computes the sense pair similarity score based on the information content of three senses: the two senses and their least common subsumer in the noun/verb hierarchy.

we cannot simply choose a hypothesis with the maximum R o − R m value, since v 3 , which is clearly not related to bank#n#1 but with a minimum R m = 100, will therefore be (erroneously) returned as the answer.


Some attempts were made to let annotators judge rule correctness directly, that is by asking them to judge the correctness of a given rule (Shinyama et al., 2002;Sekine, 2005).

this simulates the utility of rules in an application and yields high inter-annotator agreement.


We refer to this number as the upper bound on the best performing NLP system for the gold standard.

the correlation between centrality degree and hierarchal dominance will be high.


This is because when there are repeated words, annotators sometimes randomly pick removed ones.

when evaluating the system performance, we do not consider filled pauses and incomplete words since they can be easily identified and removed.


We first discuss the results for the TripAdvisor-Gold dataset shown in We use LIBLINEAR (Fan et al., 2008) with L2regulization, parameter optimized over the 80% training data (3 folds for training, 1 fold for testing).

another question one might have is whether the performance gain of PCFG features are mostly from local sequences of POS tags, indirectly encoded in the production rules.


Many of the lexical mappings are ambiguous.

morphology alone is not enough for handling key points of difference between CEA and MSA.


Note that, considering the scale of experiments (10,000 queries), around 3% improvement with respect to NDCG is a very interesting result for web search.

these patterns increase the coverage of the system by handling the queries that did not appear before but share similar structures as previous queries.


We investigate the potential of Tree Substitution Grammars as a source of features for native language detection, the task of inferring an author's native language from text in a different language.

this metric requires a probabilistic formulation of the grammar, which 2DOP does not supply.


5 The annotated tense/aspect labels include 12 combinations of tense (past, present, future) and aspect (nothing, perfect, progressive, perfect progressive).

one reason why the proposed method achieves high performance in the first type of errors is that tense errors with action verbs written as simple present are relatively easy to detect.


The confidence measure of the SVM is the distance to the separating hyperplane, and that of the MAXENT and the CRF is the marginal probability of the estimated label.


Previous research on grammatical error correction typically uses pointwise prediction that performs classification on each word independently, and thus fails to capture the information of neighboring labels.

we constructed a large-scale tense/aspect corpus from Lang-8, 3 a social networking service for learners of foreign languages.


The utterance and speaker information elements contain what is said at each dialogue turn and the corresponding character who says it, respectively.

as each movie commonly belongs to more than one single genre, much more genres are actually represented in the dataset.


In such applications, users are asked to provide their own opinions about selected issues.

for instance, given the same debate as before, a new argument T3 may be expressed by a third participant with the goal of contradicting T2 (that becomes the new H (H1) in the pair), as shown in Example 3.


For picture clarity, we introduce type 1 attacks only from A 11 .

tE is adopted to extract the abstract arguments from natural language debates and to provide the relations among these arguments; argumentation theory is then used to compute the set of accepted arguments among those obtained from the tE module, i.e., the arguments shared by the majority of the participants without being attacked by other accepted arguments.


Similar observations can be made with respect to Chambers and Jurafsky (2009) and Kasch and Oates (2010), who also study a single discourse relation (narration), and are thus more limited in scope than the approach described here.

another prospective field of application can be seen in NLP applications, where selection preferences for relation words may serve as a cheap replacement for full-fledged discourse parsing.


• When applied to words, we show that the method yields retrieval effectiveness at par with statistical stemming.

tolerating some form of derivational morphology where nouns are transformed into adjectives via the attachment of the suffix ﻱي (y) 2 (ex.


Due to Arabic's morphological complexity, Arabic retrieval benefits greatly from morphological analysis-particularly stemming.

morphological approaches assume an Arabic word is constituted from prefixes-stem-suffixes and aim to remove prefixes and suffixes.


This draws from the fact that differences in spelling variations and the construction of broken plurals are typically due to the insertion or deletion of long vowels.

they may not be able to handle infixation and spelling variations.


In this work, we provide a comparison of techniques for selecting snippets associated with the query, as well as an evaluation of methods for aligning the matching words in the query to those in the selected snippets.

perhaps the simplest approach -but also a surprisingly powerful one -is to pOS tag some relevant snippets for a given query, and then to transfer the tags from the snippet tokens to matching query tokens.


They report report significant improvements when incorporating features from the snippets.

transferring information from the snippets provides additional benefits, significantly improving even the uncased baseline taggers.


When the archives were destroyed, the tablets for the most part broke into many pieces.

any text present between brackets has been inferred from context and transcriber experience with usual phrasing in Hittite.


WordNet's definitions of the supersenses are terse, and we could find little explicit discussion of the specific rationales behind each category.

ssTs both refine and relate lexical items: they capture lexical polysemy on the one hand-e.g.,  disambiguating PERsON vs.


In future work, we plan to do an in-depth analysis of the features that best characterize the changes in word usage over time, and develop representations that allow us to track sense changes.

2000: From a New York plastic surgeon comes all anyone ever wanted to know-and never imagined-about what goes on behind the scenes at the office of one of the world's most prestigious plastic surgeons.


We then calculate the probability of each author given the model's parameters, the test text words, and the inferred author/document topic ratio and document topic distribution.

there are several key differences between DADT and AT.


Little research was done for multi-view domain adaptation.

multi-view learning aims to improve classifiers by leveraging the redundancy and consistency among these multiple views (Blum and mitchell, 1998;Rüping and Scheffer, 2005;Abney, 2002).


The observation is consistent with Koehn (2005) which claimed that there is a negative correlation between the vocabulary size and translation performance without explicitly distinguishing WSA and WSR.

different from alignment, translation is a pattern matching procedure (Lopez, 2008).


As can be seen from Table 3, the systems using character as WSA outperformed the ones using word as WSA in both small-scale (row 3-5) and large-scale task (row 6-8) with all segmentations.

experiment, our approach consistently outperformed two baselines with three different word segmenters: fully word-based system (using word for both alignment and translation) and fully character-based system, in terms of alignment quality and translation performance.


More than 70% of the unique tokens appear less than 5 times in WCorpus.

interestingly, we found that using character as WSA and BS as WSR (Char+BS), a moderate gain (+0.43 point) was achieved compared with fully BS-based system; and using character as WSA and PW as WSR (Char+PW), significant gains were achieved compared with fully PW-based system, the result of CTB segmentation in this setting even outperformed our proposed approach (+0.42 point).


For We split L 1 into (possibly empty) sublists σ i , 0 ≤ i < s, called segments, such that each σ i is the descending sublist consisting of all elements from L 1 that belong to I i .

empirically, we show a gain in running time of a standard machine translation system, at a small loss in accuracy.


In Section 2, we describe VB in more detail.

we discover that, after all training is complete, VB improves the performance of the overall system, lowering AER ( Figure 1) for all three language pairs.


Those methods can be classified into the following three types: Type-1: Conducting the target word selection and reordering jointly.

the PO-HPBMt method changes the post-ordering method of PO-PBMt from a phrase-based SMt to a hierarchical phrase-based SMt.


In this paper, we show that an unsupervised segmenter can in fact rival or surpass supervised MSA segmenters on MSA itself, while at the same time providing superior performance on dialect.

as  induces 130 prefixes and 261 suffixes for MT-08 (statistics for Morfessor are similar).


Some instances extracted from the CLC-FCE corpus have similar characteristics to the instances from the JLE corpus.

the accuracy of the proposed model consistently increased by over 10% for all three data sets.


The meta-classifier for the proposed method consists of multiple linear classifiers.

iNTEG model demonstrated better accuracy and precision for the JLE test set.


We assume that the polarity of document is closely related to some aspects for the reason that people are writing reviews to praise or criticize certain aspects.

oscar (2011) proposed to discover fine-grained sentiment with hidden-state CRF (Quattoni et al., 2007).


To be specific for each aspect, we have ⃗ w pola and ⃗ w subja representing the vector of feature weight for aspect a to calculate the polarity and subjectivity score.

each informative sentence of the document characterizes one aspect, expressing aspect specific polarity or subjective features.


We further add two background scopes, one being subjective sentences (the sentences that contain subjective words), and the entire document.

table 2 lists the results of the different kernel type combinations.


(2011) process sentences and tweets respectively.

an important subtask in sentiment analysis is sentiment classification.


Our model with global constraints of semantic roles selected correct predicate-argument structures and successfully improved performance of sentence compression.

sR labels can connect much more arguments to their predicates.


Although attempts have been made at automating the evaluation of summaries, none is so good as to remove the need for human assessors.

the results are encouraging; it seems that removing noisy topics from training data does improve the correlations with manual metrics in most cases.


3 Our individual models (generalized and domainspecific) are similar to Giménez and Màrquez (2004) in that we use a subset of their features and take onepass, left-to-right tagging approach, which is a simpler version of theirs.

(2003) introduced a POS tagging algorithm using bidirectional dependency networks, and showed the best contemporary results.


Given a word-form, we count how often each POS tag is used with the form and keep only ones above a certain threshold.

we use Liblinear for learning, which trains much faster than their classifier, Support Vector Machines.


In this way, a lemmatisation system can be trained and tested using any supervised tagging model.

lemmatisation is especially important for processing morphologically rich languages, where the number of different word forms is too large to be included in the part-ofspeech tag set.


We have presented our collection methodology and analysis of error correction logs across error types (corrected vs. uncorrected) and languages (English and Japanese).

this work was conducted during the internship of the first author at Microsoft Research.


In this approach, a first phase of string-level substitutions inserts whitespace around, for example, punctuation marks; upon completion of string rewriting, token boundaries are stipulated between all whitespace-separated substrings (and only these).

if there were a rule !wo(n't) will \1 (1) applied to the string I won't go!, all characters in the second token of the resulting string (I will n't go!)


x n−1..n with a left context χ → , we define its Right Branching Entropy (RBE) as: The Left Branching Entropy (LBE) is defined in a symmetric way: if we note χ ← the right context of x 0..n , its LBE is defined as: The RBE (resp.

we improve on (Jin and Tanaka-Ishii, 2006) by adding normalization and viterbidecoding.


3 Pseudo-error Sentences and Domain Adaptation The error corrector described in Section 2 requires paired sentences.

the link features are important for the error correction task because the system has to judge output correctness.


Adding dependency language model ("depLM") and the maximum entropy shift-reduce parsing model ("maxent") significantly improves BLEU and TER on the development set, both separately and jointly.

to improve rule coverage, we follow Shen et al.


Furthermore, the introduction of nonterminals makes the grammar size significantly bigger than phrase tables and leads to higher memory requirement (Chiang, 2007).

phrase table limit is set to 20 for all the three systems.


In the first pass, our decoder uses standard phrase-based features to build a hypergraph.

the chance of risk-free hypothesis recombination (Koehn et al., 2003) significantly decreases because complicated contextual information is much less likely to be identical.


If is "object 2 that 3 is 4 associated 5 ", ; if is "an 1 object 2 that 3 is 4 associated 5 ", .

in Ma's another paper , more linguistic features for discriminative learning are also added.


On the other hand, instead of directly merging TM matched phrases into the source sentence, some approaches (Biç ici and Dymetman, 2008;Simard and Isabelle, 2009) simply add the longest matched pairs into SMT phrase table, and then associate them with a fixed large probability value to favor the corresponding TM target phrase at SMT decoding.

it indicates the content matching status between and , and reflects the quality of .


Moreover, following the approaches of Koehn-10 and Ma-11 (to give a fair comparison), training data for SMT and TM are the same in the current experiments.

for example, there are six possible corresponding TM target phrases for the given TM source phrase "关联 4 的 5 对象 6 " (as shown in figure 1) such as "object 2 that 3 is 4 associated 5 ", and "an 1 object 2 that 3 is 4 associated 5 with 6 ", etc.


With that, we can state the nondeficient variants of IBM-3 and IBM-4.

the resulting alignment is then written as a vector a J 1 , where each a j takes integral values between 0 and I, with 0 indicating that f j has no English correspondence.


GP regression assumes the presence of a latent function, f : R F → R, which maps from the input space of feature vectors x to a scalar.

the approaches above assume that the data is split into an unstructured set of M tasks, e.g., by annotator.


A possible explanation is that in EasyAdapt the multi-task sharing parameter is set at a = 1, which may not be appropriate for the task.

this approach was more reliable than random restarts in terms of accuracy and runtime efficiency.


The post-editing effort scores were produced independently by three professional translators based on a previously post-edited translation by a fourth translator.

making use of all these layers of metadata together gives substantial further improvements, reaching the best result with Combined A,S,T .


This is due to the fact that the denominator factor must be accumulated for all higher order states that do not have the given n-gram.

table 5 presents perplexity and WER results for less heavily pruned models, where the pruning thresholds were set to yield approximately 1.5 million n-grams (4 times more than the previous models); and another set at around 5 million n-grams, as well as the full, unpruned models.


The joint approach with global features significantly outperforms the baseline and the model with only local features.

chen and Ng (2012) have proven that performing identification and classification in one step is better than two steps.


arbitrary global features over multiple local predictions.

compared to the staged baseline, it gains 1.6% improvement on trigger's F-measure and 8.8% improvement on argument's F-measure.


In comparison, our approach is a unified framework based on beam search, which allows us to exploit arbitrary global features efficiently.

we take a fresh look at this problem and formulate it, for the first time, as a structured learning problem.


Formally, the local coherence of a text T is equal to where OutDegree(s i ) is the sum of the weights associated to edges that leave s i and N is the number of sentences in the text.

moreover, future work should also examine the use of discourse relation information, as proposed in (Lin et al., 2011).


Contrary to the bipartite graph, one-mode projections are directed as they follow the text order.

table 7 shows the results obtained for the comparisons between the two first categories and the Britannica Student articles.


As our constituent trees are based on characters, we follow previous work and redefine the boundary of a constituent span by its start and end characters.

words are treated as the atomic units in syntactic parsing, machine translation, question answering and other NLP tasks.


It has been mainly exploited to solve the PP-attachment problem, motivated by psycholinguistic models.

for non-negative integers i and j with i ≤ j, we write [i, j] to denote the set {i, i + 1, .


However, at this point it must make a commitment and either construct α 3 or pop N1 from the stack (implicitly committing to α 2 ) before N2 is read from the buffer.

a second popular parser, the arc-eager model of Nivre (2003), instead adopts a mixed strategy.


In this model, a dependency h → d is constructed using a purely bottom-up strategy if it represents a left-arc, that is, if the dependent d is placed to the left of the head h in the input string.

furthermore, it is not meaningful to associate transitions la k and ra k , for any k ≥ 1, always with the same features, since the constructed arcs impinge on nodes at different depths in the involved spines.


The transition does not advance with the reading of the buffer.

a parsing strategy is called purely top-down if h → d is constructed before any dependency of the form d → i.


These operations do not change the variable tree; one can use this to show that t i has the same structure as ξ.

by evaluating these trees in the algebra, the symbols con 3 and con 4 are interpreted as concatenation, and we see that the first rule encodes the SCFG rule A → bCD, DabC .


We have already seen the b-rules for SCFGs and tree-to-string transducers in the preceding subsections; now we have a closer look at the class C for SCFGs.


Many features are derived from a long label sequence because it will contain many labels between the CP and the NPC.

the linear distortion cost model used in Moses (Koehn et al., 2007), whose costs are linearly proportional to the reordering distance, always gives a high cost to long distance reordering, even if the reordering is correct.


We initialize all parameters in s d to 0, set the learning rate for the stochastic optimizer to 0.001.

among them, (Liu et al., 2010) proposed to use phrase and rule pairs to model the context information in a loglinear framework.


This update requires n− q + 1 operations. )

the target domains in this work are twitter and Sina Weibo, and the main language pair is Chinese-English.


-Kenny Mayne，最近这么样啊！！, where an English message and its Chinese translation are in the same post, separated by a dash.

we do not have the resources to produce a carefully curated test set with a more broadly representative distribution.


We then present experiments showing that our harvested data not only substantially improves translations of microblog text with existing (and arguably inappropriate) translation models, but that it improves the translation of more traditional MT genres, like newswire.

translation models are generally robust to such kinds of errors and can learn good translations even in the presence of imperfect sentence pairs.


The fact that gLDA+SVM performs better than the standard gSLDA is due to the same reason, since the SVM part of gL-DA+SVM can well capture the supervision information to learn a classifier for good prediction, while standard sLDA can't well-balance the influence of supervision.

in training, we run the Markov chain for M iterations (i.e., the burn-in stage), as outlined in Algorithm 1.


Table 1 shows in detail the percentages of the training time (see the numbers in brackets) spent at each sampling step for gSLDA+.

it is non-trivial to develop a Gibbs sampling algorithm using the similar data augmentation idea, due to the presence of latent variables and the nonlinearity of the soft-max function.


We also contribute with a novel knapsack factor, along with a linear-time algorithm for the corresponding dual decomposition subproblem.

10 becomes that of minimizing the sum of the losses with a penalty for the (weighted) variance of the vectors {0, u1, u2, u3}, regularizing the difference towards their average, as in Evgeniou and Pontil (2004).


The number of states in the dynamic programming lattice grows exponentially with the order of the language model (Jelinek, 1998;Koehn, 2004).

this balances the contributions of the language model and the typesetting model to the posterior (Och and Ney, 2004).


3 The glyph weights, φ c , do not have a closed-form update.

the symbols are not noisy in decipherment problems and in our problem we face a grid of pixels for which the segmentation into symbols is unknown.


This requires an external module that evaluates the execution accuracy of the candidate parses.

second, another version of the base features are constructed in which nonterminal symbols include action arguments but omit all interleaving verification steps.


For the navigation task, we use the MARCO (MacMahon et al., 2006) execution module, which is also used to evaluate how well the overall system learns to follow directions (Chen and Mooney, 2011).

we modify it to rerank the parse trees generated by Kim and Mooney (2012)'s model.


18.3% of the Scenes are Participants in another Scene, 11.4% are Elaborator Scenes and the remaining are Parallel Scenes.


Our experiments show significant improvement of our new model over baselines with three evaluation metrics in the new task.

hashtags prove to be a direct indication of the semantics of tweets (Ramage et al., 2010); (2) The news specific features columbia.edu/˜weiwei such as named entities in a document.


We can conclude WTMF is a very strong baseline given that it achieves high scores with three metrics.

the goal is to predict the url referred news article based on the text in each tweet.


In this Section, we introduce how to create text-to-text relations.

they become ineffective when applied to short texts such as Twitter feeds.


Requests also stimulate extensive use of what Brown and Levinson (1987) call negative politeness: speaker strategies for minimizing (or appearing to minimize) the imposition on the addressee, for example, by being indirect (Would you mind) or apologizing for the imposition (I'm terribly sorry, but) (Lakoff, 1973;Lakoff, 1977;Brown and Levinson, 1978).

the bound- Quartile: 1 st 2 nd 3 rd 4 th Wiki 62% 8% 3% 51% SE 37% 4% 6% 46% ary between somewhat polite and somewhat impolite requests can be blurry.


Any opinions, findings, conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of NSF.

each has a demonstrable negative impact, costing essays an average of more than 0.59 points when it occurs.


This solution at least maintains locality in most cases.

a baseline approach of taking the most common class for each connective results in an accuracy of 68.70% ± 0.88%.


Moreover, supervised parsers always only simulate the treebanks they were trained on, whereas unsupervised parsers have an ability to be fitted to different particular applications.

our estimation assigns them the stop probability about 0.3 in both directions.


The quality of such tagging is not very high since we do not use any lexicons or pretrained models.

3 The STOP probability is computed in a similar way: where is the number of times a head c h had the last child c f in the direction dir in the history.


As mentioned above, we can use Equation 1 to obtain distributions over y CCG and y CF G via marginalization.

the design of these rules is a major linguistic and computational undertaking, which requires multiple iterations over the data to increase coverage (Miyao et al., 2005;Oepen et al., 2004).


Tradeoff between Target and Coarse Annotations: We also assess the relative contribution of coarse annotations when the size of annotated training corpus in the target formalism varies.

table 5 shows such features for HPSG; similar patterns are also found for the other grammar formalisms.


More importantly, in some transfer scenarios, deterministic rules are not sufficient, due to the high ambiguity inherent in the mapping.

in the BFGS algorithm (see Section 4), feature expectation is computed using the inside-outside algorithm.


All of these corpora were derived via conversion of Penn Treebank to the target formalisms.


5 For HPSG, we evaluate all types of dependencies, including punctuations.

instead, we assume that the distribution over y CF G is a loglinear model with parameters θ CF G (i.e., a subvector of θ) , namely: (3) we assume that both p joint and p CF G have the same dependence on the f CF G features.


Our results show that OSTAG outperforms both baselines.

a large effort in non-probabilistic grammar induction has been performed through manual annotation with the XTAG project (Doran et al., 1994).


(2012) showed significant translation quality gains by tuning on the bitext.

the mass is concentrated along the diagonal, probably because Mt05/6/8 was prepared by NISt, an American agency, while the bitext was collected from many sources including Agence France Presse.


AdaGrad is a method for setting an adaptive learning rate that comes with good theoretical guarantees.

the bitext5k model contains basic rules such → programme, → this programme, and → that programme.


Broader comparisons, though time-intensive, could help differentiate these methods.

naively following the FOBOS framework requires updating millions of weights.


Our work also uses bilingual information, using the source words as part of the conditioning context.

this translation task is a good test for the generality of our approach.


Theoretically, Bayesian models should outperform maximum likelihood approaches on small data sets, due to their improved modelling of un-(a) GIZA++ baseline (b) our model certainty.

in this paper we limit our focus to inducing word alignments, i.e., by using the model to infer alignments which are then used in a standard phrasebased translation pipeline.


a word occurrence) from the corpus comes from the positive class.

ssL is motivated by a simple reality: the amount of available machine-readable data is exploding, while human capacity for hand-labeling data for any given ML task remains relatively constant.


MNB uses add-1 smoothing to estimate the conditional probability of the word "resources" in each class as θ + w = 1+1 216+33504 = 5.93e-5, and implying that "resources" is a negative indicator of the Earnings class.

mNB-Fm attempts to improve the noisy estimates θ + w and θ − w utilizing the robust estimate for P (w) computed over unlabeled data.


Our second source of information consists of character and movie metadata drawn from the November 4, 2012 dump of Freebase.

they each use different evidence: since our data includes not only textual features (in the form of actions and attributes of the characters) but also non-textual information (such as movie genre, age and gender), we design a model that exploits this additional source of information in discriminating between character types; since this extralinguistic information may not always be available, we also design a model that learns only from the text itself.


Table 1 presents the VI between the learned persona clusters and gold clusters, for varying numbers of personas (P = {25, 50, 100}) and topics (K = {25, 50, 100}).

the probability that character e embodies persona k is proportional to the number of other characters in the plot summary who also embody that persona (plus the Dirichlet hyperparameter α k ) times the contribution of each observed word w j for that character, given its current topic assignment z j .


Fertility: We also add a fertility model P θ f ert to the translation model using the formula: where, P 0 represents the base distribution (which is set to uniform) in a Chinese Restaurant Process (CRP) 1 for the fertility model and C −i represents the count of events occurring in the history excluding the observation at position i. φ i is the number of source words aligned to (i.e., generated by) the target word e i .

this makes the complexity far worse (in practice) since the dimensionality of the feature vectors d is a much higher value than |V e |.


The comparable number for  Table 4: Sample (1-best) Spanish/French translations produced by the new method on the EMEA corpus using word translation models trained with non-parallel corpora.

we sample from the full set of translation candidates including additional target phrase (of size 2) candidates which results in a much larger vocabulary consisting of 1600 candidates (∼4 times the original size), yet our method runs much faster and yields better results.


If the words that precede or follow an extracted number match an entry in the dictionary, we change the semantic representation as described in the operation.

the context of a numerical expression should provide sufficient information to determine what it measures.


The performance is surprisingly good, considering the subjective nature of this task.

this study can handle any kind of target and situation that is quantified by numbers, e.g., number of people facing a water shortage.


To calculate statistical significance, we use the paired bootstrap method, which can accommodate complex evaluation metrics like F1 (Berg-Kirkpatrick et al., 2012).

model parameters are learned by the Expectation-maximization (Em) algorithm.


Importantly, the contextualization of the vectors seems to be beneficial, at least with the M&L component-wise operators.

we implemented a second method, in which we modify the KL criterion above by estimatingP E andP S from other model summaries that are drawn from the same domain (i.e.


In the current work, this particular insight is used to solve the data sparsity problem in the sentiment analysis by leveraging unlabelled monolingual corpora.

the reduction in the perplexity leads to the reduction of training documents to attain the same classification accuracy without any dimensionality reduction.


At the high-recall points, the Pattern model drops quickly in precision.

this simple process is complicated by the fact that the semantic parser requires two additional types of information for each lexical entry: a syntactic category, and a weight.


In the extreme case where a state item does not contain any unary action, the number of actions is 2n, where n is the number of words in the sentence.

as the results show, both the padding extension and semi-supervised features are more helpful on relatively large spans: the performance gaps between the three parsers are enlarged with increasing span lengths.


We employ the Brown clustering algorithm (Liang, 2005) on unannotated data (word segmentation is performed if necessary).

completed state items for the same sentence can have different numbers of unary actions.


(2010)  The objective function is not differentiable due to the hinge loss.

henceforth, after mapping each word to its vector, we represent a sentence S as an ordered list of (word,vector) pairs: Now that we have discrete and continuous representations for all words, we can continue with the approach for computing tree structures and vectors for nonterminal nodes.


The key insight is that dialog is a temporal process in which correlations between turns can be harnessed to overcome SLU errors.

figure 2: The DISCfIXED model is a traditional maximum entropy model for classification.


Given a prior distribution b(g) and a resultũ, an updated distribution b (g) can be computed by summing over all hidden user actions u: where η is a normalizing constant (Williams and Young (2007)).

discriminative approaches use conditional models, trained in a discriminative fashion (Bohus and Rudnicky (2006)) to directly estimate the distribution over a set of state hypotheses based on a large set of informative features.


These differences yield distinctions in the distributions in the two corpora (Williams (2012)).

unlike in traditional maximum entropy models such as the fixed-position model above, these features functions are dynamically defined when presented with each turn.


and Exp., the performance of exp data is inferior to that of BLLIP and their combination.

unlike their previous work, our previous work (Zhou et al., 2010) presented a method to predict the missing connective based on a language model trained on an unannotated corpus.


If it is true, the synthetic implicit relations are expected to be helpful for implicit discourse relation classification.

the polarity of each word in arguments is derived from Multi-perspective Question Answering Opinion Corpus (MPQA) (Wilson et al., 2009).


the problem of optimization becomes the minimization of the joint empirical risk written as: where loss function L(.)


Third, existing discourse parsers do not discriminate between intra-sentential (i.e., building the DTs for the individual sentences) and multisentential parsing (i.e., building the DT for the document).

this approach has been shown to be more robust and flexible across domains (Biran and Rambow, 2011; Hernault et al., 2010).


Next, in section 3, we generalize the method by introducing indicator hierarchies and explain how to learn the best models associated with them.

as indicators we used: left and right grammatical types and subtypes, entity types, a boolean indicating if the mentions are in the same sentence, and a very coarse histogram of distance in terms of sentences.


We claimed before that all mention pairs seemed not to be identically distributed since, for example, pronouns do not behave like nominals.

when using a mapping to a too small feature space, the classifier cannot capture the distribution very well: the data is too noisy.


Rising occurs when the head is not the same as the governor.

we identify three previously proposed selection methods, and compare them on the task of catenae selection for ad hoc IR.


Dependency-based techniques can also be highly effective for ad hoc information retrieval (IR) (Park et al., 2011).

adaboost.M1 was selected in preference to other algorithms because it performed better in preliminary experiments, leverages many weak features to advantage, and usually does not overfit (Schapire et al., 1997).


Beyond the immediate usability of its output and its effective use for domain Word Sense Disambiguation (Faralli and Navigli, 2012), we wish to show the benefit of GlossBoot in gloss-driven approaches to ontology learning (Navigli et al., 2011;Velardi et al., 2013) and semantic network enrichment (Navigli and Ponzetto, 2012).

individual glossaries typically provide a partial view of a given domain.


As an example, Table 2 includes results for tolerance values 0 and 15.

first, initialise the set N with the full population of annotators N .


In case t = 0, we end up with a coalition of annotators that unanimously agree with all of the categories chosen for the collective annotation.

this testset, obtaining 95% agreement between the RTE1 gold standard and their own annotation.


In Section 2 we introduce some basic terminology and argue that there are four natural forms of collective annotation.

for some annotation tasks, no integrity constraints may be known to us in advance, even though we may have reasons to believe that the individual annotators do respect some such constraints.


3 • F is independent if the collective annotation of any given item j only depends on the individual annotations of j.

by and large, the emphasis so far has been on issues such as experiment design, data quality, and costs, with little attention being paid to the aggregation methods used, which are typically limited to some form of majority vote (or taking averages if the categories are numeric).


We emphasize the fact that ParGramBank is characterized by a maximally reliable, humancontrolled and linguistically deep parallelism across aligned sentences.

with the methodology developed within XPAR, alignments can easily be recomputed from f-structure alignments in case of grammar or feature changes, so that we also have the flexible capability of allowing ParGramBank to include dynamic treebanks.


Its WordNet row gives all the reference synonyms for this entry in WordNet while its Moby row gives the first reference related words for this entry in Moby.

as this classifier can only be applied to words in context, a fixed number of representative occurrences have to be selected from our reference corpus for each neighbor of the entry.


3 Improving a distributional thesaurus The principles presented in the previous section face one major problem compared to the "classical" distributional approach : the semantic similarity of two words can be evaluated directly by computing the similarity of their distributional representations.

since we analyze texts with TreeTagger, the tagset is very close to the set of Penn Treebank tags.


Moreover, since large gains in accuracy can be achieved by spending a small amount of time just annotating word types with POS tags, we are led to conclude that time should be spent annotating types or tokens instead of developing an FST.

when deciding whether to spend time locating larger volumes of digitized text or to spend time annotating types, choose types.


Two types of humble strategies were previously attempted to build name translation components which operate in tandem and loosely integrate into conventional statistical MT systems: 1.

for crosslingual information processing applications, we should acknowledge that certain informationally critical words are more important than other common words.


All mappings φ, with φ(f ) = f for any f ∈ A and f ∈B will induce a score of −∞ since for f ∈Ã all N f f > 0 and S f f = −∞ for f ∈B.

in general, the process of translation has a wide range of phenomena like substitution and reordering of words and phrases.


Applying a holistic approach (You et al., 2012) on organizations leads to poor results, 0.06 in terms of the F1-score.

then, two graphs are iteratively aligned using the common neighbors of two entities.


Other interesting approaches such as (Klementiev and Roth, 2006;Kim et al., 2012) rely on temporal distributions of entities.

in these approaches, both 1) arbitrary translations and 2) lesser-known entities can be handled, by propagating the translation scores of known entities to lesser-known entities if they co-occur frequently in both corpora.


2) Moreover, the use of the corpora by T N could not fix this problem, and it finds another organization related to trade, '上合组织' (Shanghai Cooperation Organization).

despite the increased recall of T HB over that of T Dict , the precision of T HB is unsatisfactory because T HB maps abbreviated names such as 'WTO' with other NEs.


The collected articles from K S are translated into the target language.

some translation-based cross-lingual knowledge extraction methods have been proposed (Adar et al., 2009;Bouma et al., 2009;Adafre and de Rijke, 2006).


Transfer learning have been widely studied for classification, regression, and cluster problems.

the last category is mismatched words (22.54%).


Using variants of Wu and Palmer (1994) on the 374 SAT analogies of Turney (2005), Veale (2004) reports a success rate of 38-44% using only WordNet-based similarity.

they also offer a salutary reminder of an often overlooked fact: every comparison exploits information, but each is also a source of new information in its own right.


If they are to actively shape a creative decision, our similarity metrics must be made more generative.

turney (2005) reports up to 55% success on the same analogies, partly because his approach aims to match implicit relations rather than explicit concepts, and in part because it uses a divergent process to gather from the web as rich a perspective as it can on these latent relationships.


these models do not model debates and hence are unable to discover AD-expressions and interaction natures of author pairs.


There has been a substantial body of work on metaphor identification and interpretation (Wilks, 2007;.

in our experimental study, we use the freely available implementation of SVM in Weka (Witten and Frank, 2005).


For instance, the metaphor "tough pill to swallow" has Negative polarity as it stands for something being hard to digest or comprehend, while the metaphor "values that gave our nation birth" has a Positive polarity as giving birth is like starting a new beginning.

building computational models that can automatically identify the affect in metaphor-rich texts like "The team captain is a rock.


Because training a single network takes a lot of time, this experiment was designed to estimate the size of the hidden layer which offers good performance in tagging.

the number of necessary neurons would decrease, which assures faster training and tagging.


The final evaluation was performed in two ways.

manual inspection of some fragments suggest that the transformations inferred are rarely unjustified.


Related studies (Brooke and Hirst, 2011) have proven that topic bias is a confounding factor that results in misleading crossvalidated performance while allowing only near chance performance in practical applications.

we show that the distribution of these labels is topically biased, since they cannot be applied freely to any arbitrary article.


With the increasing amount of user generated reference texts in the web, automatic quality assessment has become a key challenge.

we showed that selecting a reliable set of positive training instances mined from the revision history improves the classification performance.


We used a sequential minimal optimization (SMO) algorithm (Platt, 1998) to train the SVMs and used different γ-values for the RBF kernel function.

(2012b) achieved an average F 1 -Score of 0.80, while reaching a higher precision than the winning team.


(2012) tackle the problem with a one-class classifier that is trained on the positive instances alone thus eradicating the need for negative instances in the training phase.

(2012) automatically identify quality flaws by predicting the cleanup templates in unseen articles with a one-class classification approach.


Our techniques significantly outperform both research and commercial state-of-the-art for these problems, including twostep linear CRF baselines which perform the two tasks sequentially.

further analysis of their corpus reveals that phonetic substitution is the primary origin of informal words in their corpus -99.2% as reported in (Wong and Xia, 2008).


The resulting noisy list is further re-ranked based on n-gram co-occurrence.

fCRf −new makes many mistakes identical to the baselines: segmenting informal words into several single-character words and chunking adjacent characters from informal and formal words together.


Other alternative frameworks such as Markov Logic Networks (MLNs) and Integer Linear Programming (ILP) could also be considered.

both models take the whole feature set described in Section 2.3.


The trained model was then applied to all other questions in the filtered corpus.

doesn't feel right, even though each entity by itself fits the template.


One explanation for this is that sometimes the instantiation filter eliminates all possible entity pairs for some relation that is incorrectly considered relevant by the algorithm.

in this paper we constrain ourselves to generate comparable questions between entities that appear in the article.


In addition, it enabled us to measure the relative contribution of the instantiation models on top of relevance model.

we eventually choose instantiation candidates, and thus preferred supervised methods that enable filtering and not just ranking.


This is in line with Table 4, which consistently illustrates CPP can get higher recall on fullstop punctuation prediction for both correctly recognized and automatically recognized texts.

it contains around 1.5M words in total and consist of various genres including weblogs, web texts, newsgroups, email, reviews, question-answer sessions, newswires, broadcast news and broadcast conversations.


Since the text used for corpus annotating are usually drawn from specific fields (e.g.

they do help to shrink the set of plausible predication candidates for each character or word.


The proposed model will also be compared with the semi-supervised pipeline S&T model described in (Wang et al., 2011).

to better understand the tagging results, we summarize the increase and decrease of the top five common tagging error patterns of our model over Baseline II for the correctly segmented words, as shown in Table 6.


Moreover they include a Metropolis-Hastings correction step, which is required to correct the samples to account for repeated substructures which will be otherwise underrepresented.

this way we don't insist on a single tiling of phrases for a sentence pair, but explicitly model the set of hierarchically nested phrases as defined by an ItG derivation.


We set the distortion limit to 6 and max-phrase-length to 7 in all experiments.

their approach does not constitute a valid Bayesian model.


Its most important advantage is that arbitrary features can be added to the model.

λ ) for Algorithm 1 1: Pre-train to obtain θ 1 = (W, W = 0, M, B) as the initial parameter 2: for all i such that 1 ≤ i ≤ M axIter do Decode with θ i on the dev set and merge all k-best-lists Run Algorithm 1 based on the merged kbest-list to obtain θ i+1 5: end for 6: Post-train based on θ M axIter+1 to obtain θ Output: θ The whole training for the AdNN model is summarized in Algorithm 2.


an element wise sigmoid function sigmoid(x) = 1/ 1 + exp(−x) .

it can transform complex language translation into feature engineering: it can achieve high translation performance if reasonable features are chosen and appropriate parameters are assigned for the weight vector.


The third data set is composed of 5 corpora 2 from LDC with various domains, including news, magazine, and finance.

the number of phase pairs generated by our method is only 73.9%, 52.7%, and 45.4% of the GIZA-batch's respectively.


The result may indicate that our hierarchical phrase combination method is sensitive to the integration order when the training data is small and there exists large gap in the similarity.

how to effectively use those data and improve translation performance becomes a challenging issue.


In all these approaches, adaptation is performed during model training, with respect to a representative development corpus, and the models are kept unchanged when then system is deployed.

we follow this technique, but want to have multiple weight vectors, adapted to different texts, between which the system switches at decoding time.


With a small modification, our framework could be changed to use a single table that stores a vector of n statistics instead of a vector of n tables.

when working with multiple and/or unlabelled domains, domain adaptation is often impractical for a number of reasons.


These include syntactic, semantic and mixed syntacticsemantic classifications (Grishman et al., 1994;Miller, 1995;Baker et al., 1998;Palmer et al., 2005;Schuler, 2006;Hovy et al., 2006).

ficient representation of each class, we collected from VerbNet the verbs for which at least one of the possible classes is represented in the 183 verbs set by at least one and at most seven verbs.


With this representation we can write: It can be shown that the first term in equation 4 increases with the quality of the selected items, and the second term increases with their diversity.

sP evaluation We explore a variant of the pseudo-disambiguation task of Rooth et al.


Note that DPPcluster runs for 17 iterations while the AC baseline performs only 6.


Many news organizations that feature financial news, such as Reuters, the Wall Street Journal and Bloomberg, devote significant resources to the analysis of corporate news.

on a small, randomly selected sample of sentences from all three sectors, two of the authors working independently evaluated the semantic parses, with approximately 80% agreement.


For this reason, we evaluated all the sense probabilities as they were.

primary evaluations compared our method with two conventional methods.


some structures like sequences or parse trees, specialized and tractable dynamic programming algorithms have proven to be very effective.

each y i is associated with a real valued c p,i ∈ which is the score for the variable y i being assigned the value 1.


We built a language model using SRILM (Stolcke, 2002) on the domain specific sources such as top wiki pages and blogs on online movie reviews, etc., to obtain the probabilities of domain-specific n-grams, up to 3-grams.

we assign labeled words to their designated topics.


This representation also factorizes over nodes and edges, enabling linear-time exact inference in GUSP.

moreover, this approach is not applicable to the questionanswering setting considered in this paper, since entity pairs in questions need not correspond to valid relational instances in the database.


Our results show that sentence length, the mean number of clauses per utterance, and the Stajner-Mitkov measure of complexity are highly informative syntactic features, that classification accuracy varies greatly by the age of the speaker, and that accuracy up to 91.7% can be achieved by support vector machines given a sufficient amount of data.

verbal cues of deception were not analyzed.


This simple labeling rule covers 1583 sentences with an F 1 score of 67.2% on the SR corpus.

we use a semantic feature generalization method that relies on taxonomies to introduce such features.


To evaluate the proposed method, we built, as test data, sets of an utterance paired with responses that elicit a certain goal emotion ( Table 5).

predicting the emotion of an addressee is useful for filtering flames or infelicitous expressions from online messages (Spertus, 1997).


This paper explores a data-driven approach to performing the two tasks.

fEAR is elicited instantly by the response, and the features induced from the addressee's utterance thereby confused the classifier.


In this case, the first two utterances are used to learn the translation model, while only the second utterance is used to learn the language model.

we first asked five human worker to produce responses to 80 utterances (10 utterances for each goal emotion).


In this evaluation, the system is provided with the utterance and the goal emotion in the test data and the generated responses are evaluated through BLEU score.

table 9: Examples of utterance-response pairs to which the system predicted wrong emotions.


If both workers regarded the Goal emotion: JOY U: 16 (I'm turning 16.

we use SRILM for the interpolation of language models.


This paper presents a method for multimodal sentiment classification, which can identify the sentiment expressed in utterance-level visual datastreams.

3 AUs can occur either by themselves or in combination, and can be used to identify a variety of emotions.


we also used two datasets for the evaluation purpose: the MPQA (wilson et al., 2005) and IQAPs (Marneffe et al., 2010) datasets.


Note that, this probability can be simply computed for the SHEM model using P(d|e) as follows: We enrich our emotional model by employing the requirement that the emotional vectors of two synonym words w 1 and w 2 should be similar.

they propagated the SO of a set of seeds through this graph.


The log-likelihood of the whole data set D in Equation (1) can be defined as follows: Replacing P (w,d,r) by the values computed using the bridged model in Equation 2results in: The above optimization problems are hard to compute due to the log of sum.

-0.4-0.5) value for sentiment orientation (Neviarouskaya et al., 2007;Neviarouskaya et al., 2009).


To illustrate the utility of the confidence value, we repeat the experiment for SO prediction by BHEM using all the words appears in enriched matrix with different confidence thresholds.

such approaches did not take into account the sentiment similarity between words.


This Figure indicates the general structure of our PSSS model.

in previous research, there is little agreement about the number and types of basic emotions.


The loss function in our evaluation is the standard Mean Square Error (MSE), but to allow a better interpretation of the results, we display its root (RMSE) in tables and figures.

in this optimisation process, we aim to enforce sparsity in the feature space but in a structured manner.


Its regularisation function ψ el (•) is defined by: where λ > 0 and α ∈ [0, 1); setting parameter α to its extremes transforms elastic net to ridge regression (α = 0) or vanilla LASSO (α = 1).

5 and 7), where we individually learn {W, β β β} and then {U, β β β}; each step of the process is a standard linear regression problem with an 1 / 2 regulariser.


Social Media contain a multitude of user opinions which can be used to predict realworld phenomena in many domains including politics, finance and health.

we emphasise that there may be smaller 1 Data collection was performed using Twitter API, http://dev.twitter.com/, to extract all posts for our target users.


If the maximum number of sentences/comments chosen were k, we brought both hs and ht to the same approximate scale as hm by dividing hs by k(k − 1)/2 and ht by k − 1. from dependency parse tree) along with computing similarity in semantic spaces (using WordNet) clearly produces an improvement in the summarization quality (+1.4 improvement in ROUGE-1 F-score).

this is an interesting extension of their previous submodular framework and while the new formulation permits more complex functions, the resulting function is still submodular and hence can be combined with the dispersion measures proposed in this paper.


Without loss of generality, we can replace the difference set S * \G i−1 with a set T i−1 = {b 0 , .

diminishing returns means that the value of an element u remains the same or decreases as S becomes larger.


2 Our performance guarantee is lower than that reported by Lin and Bilmes (2010).

in the field of constrained maximization problems, Kulik et al.


The complexity order is the same as that of the algorithm of Hsieh et al.

when 0 ≤ r ≤ 1 and 0 ≤ (G i−1 ) r , and where the second inequality is from Hölder's inequality.


We thank the anonymous reviewers and Giorgio Satta for their valuable comments.

let G = (T, N, S, R) be a Context-Free Grammar in Chomsky normal form with no useless productions, where T is a finite set of terminal symbols, N is a finite set of nonterminal symbols (disjoint from T ), S ∈ N is a distinguished nonterminal called the start symbol, and R is a finite set of productions of the form A → B C or A → w, where A, B, C ∈ N and w ∈ T .


This is the focus of this section, in which we identify a subset of CFGs, which are "almost everywhere" tight.

we can weaken our requirement so that the cases in which parameter assignment yields a non-tight PCFG are rare, or have measure zero.


In the case shown in the figure, S base \S is assigned for " /PAST-BASE" and S cont \S for " /not-CONT".

syntactic parsing for Japanese has been dominated by a dependency-based pipeline in which chunk-based dependency parsing is applied and then semantic role labeling is performed on the dependencies (sasano and Kurohashi, 2011;Kawahara and Kurohashi, 2011;Kudo and Matsumoto, 2002;Iida and Poesio, 2011;Hayashibe et al., 2011).


Compared with the standard alias detection ("Surf+HomB") approach (Hsiung et al., 2005), our proposed approach achieves significantly better performance (99.9% confidence level by the Wilcoxon Matched-Pairs Signed-Ranks Test for Acc@1).

for example, the tweet from Twitter "...把 薄 熙 来 称 作"平 西 王"或 者"不 厚"... (...call Bo Xilai"peace west king" or "buhou"...)" contains both the morph and the real target 薄熙 来 (Bo Xilai).


Our ongoing work includes identifying candidate morphs from scratch, as well as discovering morphs for a given target based on anomaly analysis and textual coherence modeling.

then we propose various novel similarity measurements including surface features, meta-path based semantic features and social correlation features and combine them in a learning-to-rank framework.


The shortness and diversity of tweets may limit the power of content analysis for semantic feature extraction.

table 1 presents the wide range of cases that are used to create the morphs.


Nevertheless, the temporal distribution assumption is effective.

our work constructs heterogeneous information networks from unstructured, noisy multi-genre text without explicit entity attributes.


We use structured logistic normal distributions to represent contextual effects.

the work in this paper is inspired by unsupervised approaches that seek to discover types of relations and events, instead of assuming them to be pre-specified; this includes research under various headings such as template/frame/event learning (Cheung et al., 2013;Modi et al., 2012;Chambers and Jurafsky, 2011;Li et al., 2010;Bejan, 2008), script learning (Regneri et al., 2010;Chambers and Jurafsky, 2009), relation learning (Yao et al., 2011), open information extraction (Banko et al., 2007;Carlson et al., 2010), verb caseframe learning (Rooth et al., 1999;Gildea, 2002;Grenager and Manning, 2006;Lang and Lapata, 2010;Ó Séaghdha, 2010;titov and Klementiev, 2012), and a version of frame learning called "unsupervised semantic parsing" (titov and Klementiev, 2011;Poon and Domingos, 2009).


We instead use parsing to get a much more focused and interpretable representation of the relationship between textually cooccurring entities; namely, that they are the source and target of an action event.

many dyad-time slices are very sparse; for example, most dyads (all but 18) have events in fewer than half the time slices in the dataset.


However, finding the top-k nearest nodes to each node requires considering its similarity to all the other nodes which requires O(n 2 ) computations and since n is usually very large, doing such is practically intractable.

we wordaligned the dev/test sets by concatenating them to a large parallel corpus and running GIZA++ on the whole set.


Jensen-Shannon Divergence is a symmetric version of contextual average mutual information (KL) which is used by (Dagan et al., 1999) as word similarity measure.

constructing such graph and doing graph propagation on it is computationally very expensive for large n-grams.


For inside context integration, we abstract the inside con-text and combine them into the PASTRs for PAS transformation.

we take the score of the MEPD model as another informative feature for the decoder to distinguish good target-side-like PASs from bad ones.


2, we use an n-gram language model based on a mixture of word and POS tokens instead of a simple wordbased language model.

wong and Dras (2009) propose using information about grammatical errors such as errors in determiners to achieve better performance while they show that its use does not improve the performance, contrary to the expectation.


It turned out that the reconstructed tree correctly groups them into the Italic, Germanic, and Slavic branches of the Indo-European family tree.

at this point, the special POS tags BOS and EOS are added at the beginning and end of each sentence, respectively.


Others (Kita, 1999;Rama and Singh, 2009) use corpus statistics for the same purpose.

the way noun phrases (NPs) are constructed is a clue to how the three branches were clustered.


It first merges Norwegian-English and Swedish-English into a cluster.

we need an efficient and effective way to analyze the experimental results.


Additionally, the flexibility of the model also allows it to attempt to produce fully grammatical sentences, something not typically handled by word-to-word normalization approaches.

we collected an additional small set of data to develop our callcenter-specific generators.


First, we produce gold standard normalized data by manually normalizing sentences to their full grammatically correct form.

a longer path corresponds to an assignment with greater probability.


To establish the extensibility of our normalization system, we present results in three different domains: Twitter posts, Short Message Service (SMS) messages, and call-center logs.

while the generic setting still manages to outperform most baselines, it did not outperform the gold word-to-word normalization.


They covered a wide variety of nominal and verbal predicates, each one having only a small number of instances.

for every argument position in the goldstandard the scorer expects a single predicted constituent to fill in.


Filling the implicit arguments of a predicate has been identified as a particular case of coreference, very close to pronoun resolution (Silberer and Frank, 2012).

we also plan to test alternative approaches to solve the arguments without explicit antecedents.


This fact explains a new trend to develop accurate unsupervised systems that exploit simple but robust linguistic principles (Raghunathan et al., 2010).

the default implicit argument of a predicate with no antecedent can change every time the algorithm finds a filler with a greater salience.


Here every source language word would have itself as its representation and every target word would map into a source word that corresponds to it.

since we are also interested in the impact of different aspects of the feature representation, we divide the features into groups as summarized in table 1 and evaluate their respective contributions to the performance of the model.


The framework corresponds closely to simple, human-readable descriptions in traditional gram-mar books.

for an accurate application of nominal derivation rules, we need a lemma list with POS and gender information.


The results are shown in Table 6: all high-coverage rules are also highly accurate.

since PNs also participate in valid derivations (e.g., Chaplin -chaplinesque), one could investigate their impact on derivations rather than omitting them.


Based on the edit history visualizations, we have manually classified the 297 essays of both batches into two categories, corresponding to the two styles build-up reuse and boil-down reuse.

our analysis gives an overview of selected aspects of the new corpus.


Both the developers of these systems as well as researchers working on the subject matter frequently claim their approaches to be searching the entire web or, at least, to be scalable to web size.

in addition to its retrieval model, ChatNoir implements two search facets: text readability scoring and long text search.


These include making sure that both the root of the clause and its subject appear in the extracted pattern, and that conjunctions between entities should not be dropped (Figure 1, 3).

in order to advance towards this goal, this paper explores the following questions: • What is a good way of using syntactic patterns to represent events for generating headlines?


SYS Thank you for calling the Cambridge Information system.

this is slightly better than other incremental systems  and much faster than state-of-the-art non-incremental systems such as SPaRKy (Walker et al., 2007).


Their approach is based on SPUD (Stone et al., 2003), a constraint satisfaction-based NLG architecture and marks important progress towards more flexible incremental surface realisation.

since CRFs are not restricted by the Markov condition, they are less constrained by local context than other models and can take nonlocal dependencies into account.


To estimate P O L and P O R , we train discriminative classifiers that predict the orientation values and use the normalized posteriors at decoding time as additional feature scores in SMT's log linear framework.

this algorithm facilitates the incremental construction of MOS which may cross rule boundaries.